Is the Wind Tunnel Obsolete?

Attached to one of the aerobar extensions on Christian Vande Velde’sCervelo P4 time trial bike is a mysterious-looking little black box. Although adding anything to a bike that increases its frontal area promises to make a rider slower, if Garmin-Cervelo aerodynamicist Robby Ketchell is right, that box could be the key to making bike racing aerodynamics a real science, rather than just a black art.

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Wind tunnel testing is an essential component of the sport. Teams use it to evaluate sponsors’ equipment, or fine-tune a rider’s position to shave drag and save valuable watts.

But beyond the cost – upward of $1,500 an hour for some of the best low-speed tunnels that are suitable for bike and component testing – there’s a significant drawback to wind tunnel testing: it doesn’t translate to the real world.

Talk to enough people in the bike industry and, perhaps after a beer or two, they’ll admit that wind tunnels are finicky and produce impossible-to-replicate results. They use simplistic environmental conditions that don’t reflect the complexity of real-world conditions, from how a rider may shift position under a hard effort to the way that crosswinds can swirl around an object.

Up until now, though, that’s the best we’ve got. “The problem with measuring anything in the field is that there’s just too much noise,” says Ketchell.

If wind tunnels produce unreliable data, and the real world is too messy, then how do we move aerodynamics testing past a best guess? Ketchell thinks he has the answer, in that little black box.

He calls it the BAT box, for biomechanical aerodynamic technology. The BAT box contains several sensors, including differential pressure sensors to gauge wind speed, as well as a humidistat, temperature gauge and barometer.

If you know those three parameters, you can calculate air density, Ketchell explains, and if you know that and wind speed, you can calculate relative velocity, which is the rider’s actual speed plus the force of whatever headwind – even from a side angle – he’s fighting against. Three-dimensional tilt sensors counteract bike sway and account for wind angle.

All that sounds neat, but Ketchell explains that it’s about $40 worth of components, commonly used in applications like hybrid cars to calculate gas mileage. The key to the system is the algorithm that he wrote that analyzes the data.

“It’s a feedback system,” Ketchell explains, that adds past data to its memory and can learn from experience – an artificial neural network. That’s the genius: rather than hand-code what a 30-mile-per-hour gust of wind from -7 degrees yaw does to the sensors and have to try to anticipate and code every condition possible (30mph at -6, -8 and so on), the system inputs the data as it happens in real time. On download, the algorithm registers it as a new event and then adds it to the system memory, constructing an ever-more detailed model of real-world conditions to edit out the noise. The result? A real-world wind tunnel.

Ketchell estimates he’s done about 51 hours of data capture so far, yielding a mountain of data to pore over. He time-synchronizes the BAT box with data capture to the Garmin head units on riders’ bikes so he can correlate biometric data like power output and heart rate to environmental conditions.

So far, he says, he’s learned a few interesting things. “The angle of the wind changes far more frequently than we thought,” he says. Minute by minute readings of wind speed and yaw angle that he showed me but asked that I not photograph revealed rapidly shifting conditions that in the space of a couple of kilometers could veer from +10 to -5 degrees wind angle even without gusts or true changes of overall wind direction.

That could help equipment partners like Cervelo and Mavic design gear that better reflects real world wind conditions, instead of targeting a theoretical range based on wind tunnel data.

Also, small changes, like a rider moving his legs side to side, have outsize effects. Ketchell says it’s a given that a rider breaking his aerodynamic position to reach for a water bottle affects aerodynamics, but the BAT box might tell them if there’s a particularly good time to do that – say, below a certain speed when it has less of an impact.

Riders often behave a bit different in the tunnel than on a real ride, so the data is more reliable. “Christian hates being in the wind tunnel,” said Ketchell. “With this, I can just put it on his bike and send him out for a four-hour ride.”

Specific climate plays a role as well, he said. The Stage 6 Solvang time trial at the Tour of California, for example, has a different environmental profile from an aerodynamic standpoint than, say, the team time trial in les Essarts on Stage 2 of the Tour de France.

Weather in any location is seasonal, of course, and Ketchell said that the team is even looking into historical weather patterns and data analysis to try to predict conditions. Even if they can’t, the BAT box allows them to do test runs and model their own data. If a rider comes, it’s a two-for-one, with a personal recon as well as the data to add to the model.

“The problem with wind tunnels is that a test or a change doesn’t linearly equate to an effect,” he says. “This system can adapt to different situations, so if we can capture the data and learn what we’re measuring we can figure out how to make it useful to the team.”

In a race like the Tour de France, the early team time trial helps set the initial classification. A good placing there means everything from putting other teams on the tactical defensive, to getting a high position for the team car in the caravan (which puts it closer to riders and offers a faster response in case of flats or other needs).

At this point, it’s impossible to judge how the BAT box might change aerodynamics and tactics in time trials. But it’s significant in that, to my knowledge, no one has tried such a sophisticated approach to bridging the gap between theoretical and real aerodynamic science. Who knows where it will lead?